A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate “guess” neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention toward important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization cross-induction, enabling minicolumn functions such as the creation of powerful hierarchical “hyperneurons” and the internal representation of the external world. We suggest building blocks to extend the microcircuit theory to network-level processing, which, interestingly, yields variants resembling the artificial neural networks currently in use. On a more speculative note, we conjecture that principles of intelligence in universes governed by certain types of physical laws might resemble ours.

中文翻译：

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大脑皮层的工作原理以及注意力试错模式学习和有用分类提取的细胞机制

智能动物大脑的一个特征是能够学习用行为适当的活跃神经元输出集合来响应活跃神经元输入集合。之前，有人提出了关于如何在新皮质锥体神经元内的细胞水平上实现这种机制的假设：顶端簇或体周输入启动“猜测”神经元放电，而基底树突根据兴奋的突触簇识别输入模式，根据奖励反馈调整集群激励强度。这种简单的机制使神经元能够以令人惊讶的智能方式学习对其输入进行分类。在这里，我们修改并扩展了这个假设。我们修改了突触可塑性规则，以与在海马区 CA1 中观察到的行为时间尺度突触可塑性 (BTSP) 保持一致，使该框架在生物物理和行为上更加合理。通过与新皮质第 1 层顶端簇的反馈连接，以自愿的方式选择用于猜测放电的神经元，从而导致树突 Ca2+突发放电的尖峰，被认为是注意力、意识处理的神经相关性。一旦学习，神经元输入分类就可以在没有自愿或有意识控制的情况下执行，从而实现分类的分层增量学习，这在我们本质上可分类的世界中是有效的。除了自愿之外，我们认为锥体神经元爆发放电可以是非自愿的，也可以通过顶端簇输入启动，引起人们对新奇和有害刺激等重要线索的注意。我们根据新皮质锥体神经元的兴奋途径将其兴奋分为四类：注意与自动、自愿/后天与非自愿。此外，我们假设锥体神经元微柱束内的树突通过去极化交叉感应耦合，从而实现微柱功能，例如创建强大的分层“超级神经元”和外部世界的内部表示。我们建议构建模块将微电路理论扩展到网络级处理，有趣的是，这会产生类似于当前使用的人工神经网络的变体。从更具推测性的角度来看，我们推测受某些类型的物理定律支配的宇宙中的智能原理可能与我们的相似。